Who: Eric Horvitz, Microsoft Research
Definition: Surprise modeling combines data mining and machine learning to help people do a better job of anticipating and coping with unusual events.
Impact: Although research in the field is preliminary, surprise modeling could aid decision makers in a wide range of domains, such as traffic management, preventive medicine, military planning, politics, business, and finance.
Context: A prototype that alerts users to surprises in Seattle traffic patterns has proved effective in field tests involving thousands of Microsoft employees. Studies investigating broader applications are now under way.

Much of modern life depends on forecasts: where the next hurricane will make landfall, how the stock market will react to falling home prices, who will win the next primary. While existing computer models predict many things fairly accurately, surprises still crop up, and we probably can't eliminate them. But Eric Horvitz, head of the Adaptive Systems and Interaction group at Microsoft Research, thinks we can at least minimize them, using a technique he calls "surprise modeling."

Horvitz stresses that surprise modeling is not about building a technological crystal ball to predict what the stock market will do tomorrow, or what al-Qaeda might do next month. But, he says, "We think we can apply these methodologies to look at the kinds of things that have surprised us in the past and then model the kinds of things that may surprise us in the future." The result could be enormously useful for decision makers in fields that range from health care to military strategy, politics to financial markets.

Granted, says Horvitz, it's a far-out vision. But it's given rise to a real-world application: SmartPhlow, a traffic-forecasting­ service that Horvitz's group has been developing and testing at Microsoft since 2003.

SmartPhlow works on both desktop computers and Microsoft PocketPC devices. It depicts traffic conditions in Seattle, using a city map on which backed-up highways appear red and those with smoothly flowing traffic appear green. But that's just the beginning. After all, Horvit­z says, "most people in Seattle already know that such-and-such a highway is a bad idea in rush hour." And a machine that constantly tells you what you already know is just irritating. So Horvitz and his team added software that alerts users only to surprises--the times when the traffic develops a bottleneck that most people wouldn't expect, say, or when a chronic choke point becomes magically unclogged.

But how? To monitor surprises effectively, says Horvitz, the machine has to have both knowledge--a good cognitive model of what humans find surprising--and foresight: some way to predict a surprising event in time for the user to do something about it.

Horvitz's group began with several years of data on the dynamics and status of traffic all through Seattle and added information about anything that could affect such patterns: accidents, weather, holidays, sporting events, even visits by high-profile officials. Then, he says, for dozens of sections of a given road, "we divided the day into 15-minute segments and used the data to compute a probability distribution for the traffic in each situation."

That distribution provided a pretty good model of what knowledgeable drivers expect from the region's traffic, he says. "So then we went back through the data looking for things that people wouldn't expect--the places where the data shows a significant deviation from the averaged model." The result was a large database of surprising traffic fluctuations.

Once the researchers spotted a statistical anomaly, they backtracked 30 minutes, to where the traffic seemed to be moving as expected, and ran machine-­learning algorithms to find subtleties in the pattern that would allow them to predict the surprise. The algorithms are based on ­Bayesian modeling techniques, which calculate the probability, based on prior experience, that something will happen and allow researchers to subjectively weight the relevance of contributing events (see TR10: "Bayesian Machine Learning," February 2004).

I own "transportationalert.com" and i would like to incorporate this "surprise modeling" into my website to provide exactly this kind of service. Does anyone have any suggestions or leads on where I can implement this type of feature in my website?

thanks!

2559 Days Ago

02/29/2008

Check out "Bayesian statistics" and "Bayesian statistics traffic" on Google for some leads.

2454 Days Ago

06/13/2008

Hello,

I used to work for a company called Informeta.net that has products in this area.

Please email Ron Coleman who is the brain behind this company for further information. He can be reached at ron.coleman@marist.edu.

Thank You,

Nishal

s_nishal@hotmail.com

2272 Days Ago

12/12/2008

I see that you posted about 10 months ago on technologyreview about your traffic info webiste and a search for predictive resources.

Did you look at www.INRIX.com?

Mark Chernisky

Fairfax, VA

mchernisky@corsec.com

2559 Days Ago

02/29/2008

Traffic

Have the researchers modelled the effect of wide deployment? I'd expect that users will see no benefit when there are lots of them.

2559 Days Ago

02/29/2008

traffic

When many users utilize this service the program will predict his own recomandation. The error will propagate in the next prediction.

2558 Days Ago

03/01/2008

Suprise turns into order

In the movie "Pi" a crazy inventor was desperately trying to develop a powerful computer predicting stock exchange trades. In some time the predictive power reached 100% of accuracy because all predictions were spied furtively by the exchanges' dealers from the garbage can with printouts and closely followed by those dealers in trades.

2558 Days Ago

03/01/2008

Science fiction becomes fact. When Asimov wrote the Foundation Series, his protagonist invents psychohistory using the laws of mass action to predict the future. I was charmed that Eric Horvitz did not downplay the potential of his new predictor.

http://en.wikipedia.org/wiki/Foundation_series

2547 Days Ago

03/12/2008

Related

http://smart-city.re-configure.org is related to this topic and will be published as a chapter in a book called "Collective Intelligence: Creating a Prosperous World at Peace" emphasizing the combination of tech plus the populus to feed into systems that become a series of gradual resolution-generating activities.

2334 Days Ago

10/11/2008

Help

I would like to apply surprise modeling to another application area other than traffic forecasting. Also, I was looking for more research papers on this modeling technique. Can someone please guide me in the right direction ?